• Korean Journal of Radiology
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• 제22권6호
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• pp.970-982
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• 2021

Dual-energy CT (DECT) provides insights into the material properties of tissues and can differentiate between tissues with similar attenuation on conventional single-energy imaging. In the conventional CT scanner, differences in the X-ray attenuation between adjacent structures are dependent on the atomic number of the materials involved, whereas in DECT, the difference in the attenuation is dependent on both the atomic number and electron density. The basic principle of DECT is to obtain two datasets with different X-ray energy levels from the same anatomic region and material decomposition based on attenuation differences at different energy levels. In this article, we discuss the clinical applications of DECT and its potential robust improvements in performance and postprocessing capabilities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권1호
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• pp.221-237
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• 2016

The wireless body area networks (WBANs) consist of wearable computing devices and can support various healthcare-related applications. There exist two crucial issues when WBANs are utilized for healthcare applications. One is the protection of the sensitive biometric data transmitted over the insecure wireless channels. The other is the design of effective medical management mechanisms. In this paper, a secure medical information management system is proposed and implemented on a TinyOS-based WBAN test bed to simultaneously address these two issues. In this system, the electronic medical record (EMR) is bound to the biometric data with a novel fragile zero-watermarking scheme based on the modified visual secret sharing (MVSS). In this manner, the EMR can be utilized not only for medical management but also for data integrity checking. Additionally, both the biometric data and the EMR are encrypted, and the EMR is further protected by the MVSS. Our analysis and experimental results demonstrate that the proposed system not only protects the confidentialities of both the biometric data and the EMR but also offers reliable patient information authentication, explicit healthcare operation verification and undeniable doctor liability identification for WBANs.

• 한국정보시스템학회지:정보시스템연구
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• 제29권2호
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• pp.243-263
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• 2020

Purpose The purpose of this study is to analyze the causal relationship between user satisfaction, expected satisfaction, quality of service, perceived value, and expertise that should be emphasized in personalized medical information services for the development of personalized medical information services based on big data analysis and the spread of their demand. Design/methodology/approach This study established research models and hypotheses on the basis of the theory of reuse intent, and applied the PLS methodology for analysis, the factors that make it applicable to personalized medical services in the theory of service quality and satisfaction. Findings According to the empirical analysis result, this study confirmed that it can be seen that the expertise, perceived value, and quality of medical services did not directly affect the user's intention to reuse, but formed a direct causal relationship through variables such as whether they met expectations.

• 한국융합학회논문지
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• 제10권9호
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• pp.91-98
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• 2019

이 연구는 최근 성형을 원해 우리나라로 들어오는 외국인 환자들을 응대하는 서울 소재 성형외과를 중심으로 의료관광 활성화를 위한 모바일 헬스케어 애플리케이션 활용의 필요성 인식과 향후 기대효과를 알아보기 위해서 성형외과 직원들을 대상으로 연구하였다. 데이터 분석을 위해 SPSS 21.0 프로그램을 사용하였다. 개인특성 및 의료기관 특성에 따라 모바일 헬스케어 애플리케이션 활용의 향후 기대효과를 분석함으로써 의료관광 활성화에 도움이 될 방안을 모색하였다. 그 결과 응답자의 미래 모바일 사용 의지가 높을수록, 소속된 의료기관의 외국인 환자 숫자가 많을수록 모바일 헬스케어 애플리케이션의 활용 기대가 유의한 정(+)의 관계인 것으로 추정되었다. 또한, 모바일 헬스케어 애플리케이션의 필요성을 높게 평가할수록 앞으로 의료관광 활성화에 도움이 될 것이라는 응답이 많았으며, 미래 기대 전망에 대한 긍정적 인식도 높았다. 이 연구가 의료관광 활성화를 위해 의료종사자들의 수요분석과 서비스 활용에 관하여 모바일 서비스 앱의 지속적 추진과 확대를 위한 IT 기반이 마련되길 기대해 본다.

• 한국정밀공학회지
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• 제23권11호
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• pp.24-33
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• 2006

• 한국의학물리학회:학술대회논문집
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• 한국의학물리학회 2002년도 Proceedings
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• pp.42-47
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• 2002

No Abstract, See Full Text

• 한국고분자학회지:고분자과학과기술
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• 제25권5호
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• pp.402-410
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• 2014

• 한국진공학회:학술대회논문집
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• 한국진공학회 2013년도 제44회 동계 정기학술대회 초록집
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• pp.498-498
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• 2013

Atmospheric Pressure Plasmas have pioneered a new field of plasma for biomedical application bridging plasma physics and biology. Biological and medical applications of plasmas have attracted considerable attention due to promising applications in medicine such as electro-surgery, dentistry, skin care and sterilization of heat-sensitive medical instruments [1]. Traditional approaches using electronic devices have limits in heating, high voltage shock, and high current shock for patients. It is a great demand for plasma medical industrial acceptance that the plasma generation device should be compact, inexpensive, and safe for patients. Microwave-excited micro-plasma has the highest feasibility compared with other types of plasma sources since it has the advantages of low power, low voltage, safety from high-voltage shock, electromagnetic compatibility, and long lifetime due to the low energy of striking ions [2]. Recent experiment [2] shows three-log reduction within 180-s treatment of S. mutans with a low-power palm-size microwave power module for biomedical application. Experiments using microwave plasma are discussed. This low-power palm-size microwave power module board includes a power amplifier (PA) chip, a phase locked loop (PLL) chip, and an impedance matching network. As it has been a success, more compact-size module is needed for the portability of microwave devices and for the various medical applications of microwave plasma source. For the plasma generator, a 1.35-GHz coaxial transmission line resonator (CTLR) [3] is used. The way of reducing the size and enhancing the performances of the module is examined.

• Genomics & Informatics
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• 제19권2호
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• pp.17.1-17.13
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• 2021

Breast cancer is one of the leading causes of cancer in women all over the world and accounts for ~25% of newly observed cancers in women. Epigenetic modifications influence differential expression of genes through non-coding RNA and play a crucial role in cancer regulation. In the present study, epigenetic regulation of gene expression by in-silico analysis of histone modifications using chromatin immunoprecipitation sequencing (ChIP-Seq) has been carried out. Histone modification data of H3K4me3 from one normal-like and four breast cancer cell lines were used to predict miRNA expression at the promoter level. Predicted miRNA promoters (based on ChIP-Seq) were used as a probe to identify gene targets. Five triple-negative breast cancer (TNBC)-specific miRNAs (miR153-1, miR4767, miR4487, miR6720, and miR-LET7I) were identified and corresponding 13 gene targets were predicted. Eight miRNA promoter peaks were predicted to be differentially expressed in at least three breast cancer cell lines (miR4512, miR6791, miR330, miR3180-3, miR6080, miR5787, miR6733, and miR3613). A total of 44 gene targets were identified based on the 3'-untranslated regions of downregulated mRNA genes that contain putative binding targets to these eight miRNAs. These include 17 and 15 genes in luminal-A type and TNBC respectively, that have been reported to be associated with breast cancer regulation. Of the remaining 12 genes, seven (A4GALT, C2ORF74, HRCT1, ZC4H2, ZNF512, ZNF655, and ZNF608) show similar relative expression profiles in large patient samples and other breast cancer cell lines thereby giving insight into predicted role of H3K4me3 mediated gene regulation via the miRNA-mRNA axis.

• Korean Journal of Radiology
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• 제25권3호
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• pp.224-242
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• 2024

The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application. This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.